Abstract

Nonhealing diabetic foot ulcers (DFUs) are a common and costly complication of diabetes. Microbial burden, or “bioburden,”
is believed to underlie delayed healing, although little is known of those clinical factors that may influence microbial load,
diversity, and/or pathogenicity. We profiled the microbiomes of neuropathic nonischemic DFUs without clinical evidence of
infection in 52 individuals using high-throughput sequencing of the bacterial 16S ribosomal RNA gene. Comparatively, wound
cultures, the standard diagnostic in the clinic, vastly underrepresent microbial load, microbial diversity, and the presence
of potential pathogens. DFU microbiomes were heterogeneous, even in our tightly restricted study population, but partitioned
into three clusters distinguished primarily by dominant bacteria and diversity. Ulcer depth was associated with ulcer cluster,
positively correlated with abundance of anaerobic bacteria, and negatively correlated with abundance of Staphylococcus. Ulcer duration was positively correlated with bacterial diversity, species richness, and relative abundance of Proteobacteria,
but was negatively correlated with relative abundance of Staphylococcus. Finally, poor glycemic control was associated with ulcer cluster, with poorest median glycemic control concentrating to
Staphylococcus-rich and Streptococcus-rich ulcer clusters. Analyses of microbial community membership and structure may provide the most useful metrics in prospective
studies to delineate problematic bioburden from benign colonization that can then be used to drive clinical treatment.

Fifteen percent of those with diabetes will develop at least one diabetic foot ulcer (DFU) during their lifetime (1). Effective treatment strategies for DFUs are still lacking. Systemic and topical antimicrobial treatments are commonly used,
even though minimal evidence supports their efficacy in DFU treatment (2). Clinical signs and symptoms of infection cannot be reliably used in chronic wounds to direct antibiotic treatment (3), and the fine line between benign colonization and problematic bioburden from which to direct antibiotic treatment remains
unclear. Targeting microbial populations to promote healing and deter infection-related complications might be a novel treatment
option.

To establish the role of bacteria in impaired healing and infection-related complication, it is necessary to define the full
array of microorganisms colonizing the DFU. Clinical practice and most studies of DFU bioburden have relied on cultivation-dependent
methods, which are biased toward those microorganisms that thrive in isolation under laboratory conditions. Importantly, cultivation-based
methods tend to overlook slow-growing fastidious bacteria such as anaerobes, a subset of bacteria thought to be particularly
damaging to the wound environment (4). Genomic approaches to analyze microbial communities, such as sequencing of the small subunit 16S ribosomal RNA (rRNA) gene,
are increasingly accessible and provide much greater resolution by eliminating biases associated with culturing bacteria (5–7). The 16S rRNA gene is present in the genomes of all prokaryotes and contains sequences that allow identification and classification
of the organism. The gene also contains highly conserved regions that allow broad-range amplification by PCR (8,9). Three dimensions of bioburden may be important in the nonhealing DFU: microbial load; microbial diversity; and/or pathogenicity
(10). Microbial load is the total quantity of microbes present. Microbial diversity is the number of different bacterial taxa
present. Potential pathogens of the DFU are believed to include Staphylococcus, Streptococcus, Proteobacteria (a phylum of Gram-negative bacteria), and anaerobic bacteria (4). Genomic methods to evaluate bioburden allow these dimensions to be considered together as the microbial community and structure
within a wound. This ability represents a significant step forward in our quest to understand the role that the microbiota
play in DFU outcomes and complications.

Previous studies of the chronic wound microbiome using 16S rRNA gene sequencing used study designs and methods that combined
and analyzed heterogeneous types of wounds, including ischemic, neuropathic, and mixed-type DFUs (7,11,12). Pathophysiologically distinct DFUs likely lead to a different host/wound environment, ultimately confounding identification
of microbial populations associated with DFUs and estimations of DFU microbial diversity. Furthermore, previous studies have
not related the DFU microbiota to clinical factors found to be associated with nonhealing. In the absence of useful culture
data and the inability to access molecular techniques, clinical factors may be useful for identifying individuals at risk
for problematic bioburden because the wound environment may support or deter particular microbiota (7), which then may lead to poor ulcer outcomes. Potential patient and/or ulcer factors found to be associated with failure
to heal include wound surface area, ulcer grade, arterial perfusion, necrosis, and wound duration (13–16). Although glycemic control was found to be associated with wound healing in persons with diabetes (17), it has not been found to be associated with healing of DFUs (18). Clinical factors need to be systematically examined to determine which may serve as clinically relevant biomarkers of problematic
bioburden.

Here, we report the first systematic exploratory analysis of the microbiome of a homogenous sample of neuropathic nonischemic
DFUs (N = 52 subjects) by high-throughput sequencing of the 16S rRNA gene in relation to clinical factors that may influence the
microbiome. Specific aims were to compare the three dimensions of DFU bioburden detected by 16S rRNA gene sequencing to those
obtained with traditional quantitative cultures, to examine the microbial community structure of DFUs, and to examine clinical
factors associated with DFU microbiome.

RESEARCH DESIGN AND METHODS

Design, setting, and sample.

This study used a cross-sectional design. Subjects with DFUs were assessed for microbiota colonizing the DFU using traditional
cultures and 16S gene sequencing methods. Clinical factors were concurrently measured. Study protocols were approved by the
University of Iowa Institutional Review Board, the National Institutes of Health Office of Human Subjects Research, and the
University of Pennsylvania Institutional Review Board. Data were collected at the University of Iowa Hospitals and Clinics.
A convenience sample of subjects was enrolled using the following criteria: 18 years of age or older; plantar neuropathic
DFU; free of systemic antibiotics over the course of the past 2 weeks; negative for clinical signs of infection; negative
for wound deterioration; and negative for osteomyelitis. Plantar neuropathic DFUs were defined as open lesions on the plantar
surface of the foot exclusive of the lesser toes in persons with diabetes, insensate to 10-g monofilament on the plantar surface
of the foot on one or more plantar locations, and toe–brachial indexes >0.5. Enrolled subjects signed a written informed consent.

Clinical factors.

Protocols for measuring clinical factors have been published elsewhere (19–22). Level of glycemic control was measured as hemoglobin A1c values. Wound tissue oxygen was measured using transcutaneous oxygen measures (Model TCM400; Radiometer America) on the dorsum
of the ipsilateral foot. Necrotic tissue was measured using an item from the Pressure Sore Status Tool (23). Ulcer size, including surface area and depth, was measured using digital images and proprietary software, which produces
surface area, depth, and volume measures (22). Duration of the study ulcer was measured as the number of weeks from the time of soft tissue loss to the baseline visit
obtained through subject report and review of medical records.

Ulcer cultures.

Ulcer specimens were obtained using the Levine technique and established protocols (19). We have demonstrated this technique to have accuracy (area under the receiver-operator characteristic curve) of 0.80 when
compared with culture results based on tissue specimens (19). The DFU was cleansed with nonbacteriostatic saline and an Amies with charcoal transport swab (Copan, Brescia, Italy) was
rotated over a 1-cm2 area of viable non-necrotic wound tissue for 5 seconds using sufficient pressure to extract wound tissue fluid. Swabs were
vortexed in 1 mL tryptic soy broth, and then diluted and plated on Columbia blood agar (Remel), eosin-methylene blue agar
(Remel), CHROMagarMRSA (BD), and Brucella Agar supplemented with blood, hemin, and vitamin K (Remel). Organisms that grew on eosin-methylene blue agar plates and stained
Gram-negative were further identified using Vitek Legacy (Biomerieux).

16S rRNA gene sequencing, quality control, and analysis.

DNA was isolated from the DFU samples as previously described (24). Briefly, the swab was placed in 300 μL yeast cell lysis solution (from Epicentre MasterPure Yeast DNA Purification kit)
and 0.5 μL ReadyLyse lysozyme solution (Epicentre) was added before incubation for 1 h at 37°C with shaking. Samples were
then processed in a TissueLyser (Qiagen) at maximum speed for 2 min, followed by 30-min incubation at 65°C with shaking; 150
μL protein precipitation reagent (Epicentre) was added and samples were spun for 10 min at maximum speed. The supernatant
was removed and mixed with 500 μL isopropanol and applied to a column from the PureLink Genomic DNA Mini Kit (Invitrogen).
Subsequently, the instructions for the Invitrogen PureLink kit were followed exactly, and DNA was eluted in 50 μL elution
buffer (Invitrogen). The 16S rRNA gene was amplified from 2 μL of each DFU DNA sample using forward primer 27F and reverse
primer 534R containing a unique error-correcting barcode. PCR using the Accumprime kit (Invitrogen) was performed in duplicate
to reduce potential amplification bias attributable to the complex mixture of template. Cycling conditions were: 95°C for
2 min, followed by 30 cycles of 95°C for 20 s, 56°C for 30 s, and 72°C for 5 min. PCR products were purified with the Agencourt
AMPure kit following manufacturer’s instructions (Beckman Coulter). Negative (no template and mock) controls were treated
similarly and failed to produce a visible PCR product or sequencing reads; 50 ng of each PCR product was pooled, and the pool
was purified using the MinElute PCR Purification kit (Qiagen) according to manufacturer’s instructions. Pyrosequencing was
performed on the Roche 454 FLX Titanium Instrument at the National Institutes of Health Intramural Sequencing Center.

Sequence quality control and analysis were performed using the mothur package version 1.23 (25). Sequences were removed if they contained ambiguous bases, more than eight homopolymers, primer and/or barcode mismatches,
or were <200 nt long. Low-quality sequences were removed using the criteria of average quality score of >35 over 50-nt sliding
windows. Sequences were aligned to the SILVA reference set using mothur’s NAST-based aligner. Chimeras were identified and
removed using the mothur implementation of UChime (26) and the chimera-free GOLD reference dataset (27). Sequences were assigned to operational taxonomic units (OTUs) using an average-neighbor clustering algorithm at a threshold
of 0.03 (28). OTUs are molecular proxies for describing organisms based on their phylogenetic relationships to other organisms. Because
α and β diversity metrics are sensitive to sampling effort, we standardized the number of sequences per sample by random subsampling
using the subsample in mothur. OTUs were assigned to taxonomy using the mothur-implemented naïve Bayesian classifier trained
on the Ribosomal Database Project taxonomy training set 4 (29). Staphylococcus OTUs were speciated using pplacer (30) and a custom curated collection of Staphylococcus reference sequences.

To measure microbial load, quantitative real-time PCR assays of the 16S rRNA gene were performed as previously described (6,31).

Data analysis.

Culture findings were compared with sequencing findings using paired t tests (two-tailed; α = 0.05). Agreement in the predominant organism identified by the two methods was examined with kappas.
Clustering was performed by partitioning around medoids (PAM). PAM clusters samples by minimizing the distance between samples
in a cluster. Each cluster is defined by a point designated as the center (the “medoid”). The input to PAM was a Euclidean
distance matrix of normalized species-level OTU counts. Euclidean distance is the straight-line “ordinary” distance between
two objects in multidimensional space. Validity of clustering and number of centroids to choose for the data were determined
using the average silhouette score, for which a higher score indicates better quality and more natural clustering (32). Clusters were examined for associations with three dimensions of bioburden using Kruskal-Wallis test. Post hoc pair-wise
comparisons were completed using Wilcoxon rank-sum tests (two-tailed; α = 0.05). Covariation of species within a DFU were
examined with Spearman correlation coefficients (ρ), a measure of statistical dependence between two variables for which a
value of 1 indicates complete positive correlation and −1 indicates complete negative correlation. Significance of ρ was assessed
using false discovery rate control (q = 0.05). Associations between bioburden and clinical factors were assessed using Spearman rank correlation coefficients (ρ).
Overall associations of clinical factors with microbiome community structure and membership were calculated using analysis
of molecular variance in the freely available mothur software package. Kruskal-Wallis test was used to assess differences
in Euclidean clusters and associations with clinical factors. Post hoc pair-wise comparisons were completed using Wilcoxon
rank-sum tests.

RESULTS

Subjects and DFU microbiome measures.

Fifty-two subjects were enrolled with a mean age of 53.9 (±11.89) years. Forty-three (82.7%) were male and 48 (92.3%) were
white. Forty-three (82.7%) had type 2 diabetes, whereas the remainder had type 1 diabetes. All subjects (100%) had sensory
neuropathy. Forty-two (80.8%) of the DFUs were located on the plantar forefoot. The remaining DFUs were located on the plantar
mid foot or hind foot. Mean toe–brachial index was 0.85 (±0.26), indicating no significant problems with arterial perfusion
and that these ulcers were primarily neuropathic. The mean hemoglobin A1c was 8.5% (±2.07), the mean wound tissue oxygen was 49.3 mmHg (±9.23; n = 51), the mean ulcer surface area was 2.0 cm (±2.92), the mean ulcer duration was 34.6 weeks (±42.56), and the mean ulcer
depth was 0.2 cm (±0.26). Twenty-five ulcers (48%) had no depth or volume. Forty-two (80.8%) ulcers had no necrotic tissue
in the wound bed. Five (9.6%) had <25% wound bed necrotic tissue; one (1.9%) had 25–50% necrotic tissue and four (7.7%) had
75–100% necrotic tissue.

We surveyed DFU microbiomes by pyrosequencing the V1–V3 hypervariable regions of the bacterial 16S rRNA gene. We generated
300,660 high-quality sequences, with an average of 5,634 sequences per sample. A total of 13 phyla were identified, but the
majority of sequences classified to Firmicutes (67%), Actinobacteria (14%), Proteobacteria (9.8%), Bacteroidetes (7.3%), and
Fusobacteria (1.4%). Clustering sequences into species-level OTUs at a threshold of 97% identity revealed 867 OTUs across
all samples. After normalizing the number of sequences present in each sample by random subsampling, 477 OTUs were included
for further analyses. The average number of OTUs per sample was 30, with a range of 7–64. The most abundant OTU was classified
as Staphylococcus and was present in 49 of 52 DFU samples, comprising 29.6% of the total sequences. Because Staphylococcus aureus is believed to be particularly pathogenic to the DFU, we performed a phylogenetic speciation analysis to distinguish among
Staphylococcus species. The majority of Staphylococcus sequences (96.5%) were classified as S. aureus. Only 0.4% of the sequences were determined to be Staphylococcus epidermidis, a skin commensal. The second and third most abundant OTUs were Streptococcus (8.8% of the total sequences; present in 15 DFUs) and Lactococcus (3.9% of the total sequences; present in 38 DFUs), respectively.

Culture-based assays underestimate bioburden of DFUs.

In addition to community profiling of the 16S rRNA gene, we performed culture-based assessments of DFU bioburden. We first
compared measures of microbial load using a colony-forming unit culture-based estimate and a 16S rRNA quantitative PCR-based
estimate. We found that, on average, culturing underestimated bacterial load by 2.34 logs (P < 0.0001), and in some cases >6 logs (Fig. 1A). For each DFU, we also compared the number of species recovered by culture methods to the number of species-level OTUs recovered
by sequencing of the 16S rRNA gene. Culture-based techniques failed to capture, on average, 26 bacterial species per DFU (P < 0.0001) (Fig. 1B).

Comparison of cultivation-based data to genomic 16S rRNA gene data for characterizing DFU bioburden. A: Prediction of bacterial load (log-transformed) by total counts of colony-forming units (CFUs; circles) compared with estimating
bacterial load based on quantitative PCR (qPCR) of the 16S rRNA gene (diamonds). B: Number of different isolates recovered by culturing (circles) vs. the number of species-level OTUs by 16S rRNA gene sequencing
(diamonds). Each point represents a DFU sample and the line through the points represents the median of the data.

We also compared the relative abundance of organisms believed to be potential pathogens including Staphylococcus, anaerobes, Proteobacteria, and Streptococcus. Culturing overestimated the relative abundance of Staphylococcus, on average, by >15 percentage points (0.47 vs. 0.32; P = 0.0001), whereas culturing underrepresented anaerobic bacteria, on average, by 7.3 percentage points (0.11 vs. 0.18; P = 0.0063). Overall, the agreement between the two methods in identifying the predominant organism was 0.45 (κ: 95% CI = 0.30–0.61),
indicating only fair agreement (33). Cultures identified Staphylococcus as the predominant organism in 24 (46%) of the DFUs as compared with 20 (39%) of the DFUs by 16S rRNA gene sequencing. Based
on cultures, the number of DFUs that contained S. aureus regardless of abundance was 21 (40%), whereas 16S rRNA gene sequencing revealed that 49 (94%) of the ulcers contained S. aureus (Table 1). By culture, anaerobes were identified as the predominant organism in 6 (12%) of the DFUs, whereas sequencing identified
anaerobes as the predominant organism in 12 (23%) of the DFUs. Culturing revealed that 14 (27%) of DFUs contained anaerobes
as compared with 52 (100%) DFUs containing anaerobes by sequencing (Table 1). Proteobacteria and Streptococcus were cultured from 18 (35%) and 19 (37%) ulcers, respectively. This is in contrast to 16S rRNA sequencing, which revealed
Proteobacteria and Streptococcus in 52 (100%) and 43 (83%) DFUs, respectively (Table 1). In only four DFUs did cultures capture an isolate that was not represented by 16S rRNA sequencing. Taken together, these
results led us to rely on 16S rRNA sequence data for further analyses and to examine the relationship between DFU microbiome
and clinical variables.

Comparison of the percentage of DFUs containing different types of bacteria as assessed using culture techniques vs. 16S rRNA
gene sequencing

DFU microbiomes are heterogeneous.

To compare bacterial diversity colonizing individual DFUs, we calculated the Shannon index, an ecological measure of diversity
that incorporates the total number of different OTUs and the relative proportions of those OTUs. Higher Shannon index values
indicate greater diversity. The average Shannon index was 1.90, with a range of 0.25–3.43, suggesting great heterogeneity
in the diversity colonizing individual DFUs (Fig. 2). Examination of the relative abundance of bacterial taxa was not immediately insightful, because there also appeared to
be great heterogeneity in the taxa colonizing individual DFUs. We observed that, in general, DFUs were characterized by high
relative abundance of Staphylococcus, Streptococcus, or neither. On closer examination of this third subset of DFUs with neither abundant Staphylococcus nor Streptococcus, taxonomic analysis at higher ranks revealed prevalent anaerobic bacteria or Proteobacteria in most of the samples (Fig. 2).

Clustering of DFUs according to Euclidean distance of the 72 species-level OTUs present in >10% of DFU samples and containing
>100 sequence counts. A: Depicted is clustering of k = 3 medoids on the first two principle components, which together explain 83.19% of the point
variability. The three clusters, or EUCs, are highlighted by different color ellipses, and the points within the clusters
are represented by different symbols. The silhouette score for the clusters (average width) was 0.42. EUCs differed significantly
by Kruskal-Wallis test (P ≤ 0.05) in Shannon diversity (B), OTU richness (C), log bacterial load (D), and relative abundance of Proteobacteria (E), Staphylococcus (F), anaerobic bacteria (G), and Streptococcus (H). Color of boxes in (B–H) correspond to color of ellipse encircling each EUC. B–H: The x-axis labels represent EUC clusters, boxes represent interquartile range, lines within the box depict median, and whiskers
represent the lowest and highest values within 1.5-times the interquartile range. *Significantly different pair-wise comparison
(P ≤ 0.05) by Wilcoxon rank-sum test.

To gain insight into bacterial community interactions in DFUs, we examined covariation of species-level OTUs. We calculated
Spearman correlation coefficients for the relative abundance of the 72 OTUs that were present in >10% of the samples and contained
at least 100 sequences across all samples (Supplementary Fig. 1). We detected significant positive correlation between relative abundance of multiple anaerobic bacteria, including those
belonging to OTUs classified as Porphyromonas, Anaerococcus, Finegoldia, Peptoniphilus, Prevotella, and Incertae Sedis XI. The second prominent pattern we observed was that S. aureus abundance was negatively correlated with abundance of many anaerobes but positively correlated with relative abundance of
a Corynebacterium OTU (Supplementary Fig. 1, Supplementary Table 1; ρ = 0.47; P < 0.001). These findings were consistent with clustering analyses of community structure, because EUC2 was characterized
by high relative abundance of Staphylococcus and corresponding low relative abundance of anaerobes.

Ulcer duration, depth, and glycemic control are associated with DFU microbiome.

To determine if clinical factors were associated with different aspects of bioburden, we selected six variables measured in
DFU subjects at time of enrollment: hemoglobin A1c (a measure of glycemic control over 6 weeks), mean tissue oxygenation (a measure of arterial perfusion), ulcer duration before
sampling, ulcer depth, ulcer surface area, and necrotic tissue. Analysis of molecular variance (34) suggested that some of these clinical factors were associated with microbiome community structure (as measured by the Θ
distance metric and the weighted UniFrac metric) and/or community membership (as measured by the Jaccard distance metric and
the unweighted UniFrac metric) (Supplementary Table 2). We therefore proceeded with further analyses to validate these findings.

DISCUSSION

To our knowledge we are the first to show that the microbiome colonizing DFUs is associated with clinical factors. In addition,
we demonstrate that neuropathic DFUs cluster into groups distinguished by the dimensions of bioburden that historically have
been believed to be of importance (microbial load, diversity, and presence of pathogens). Nonetheless, these findings need
to be validated in larger independent samples of neuropathic DFUs because this study included only 52 DFUs. Finally, like
others, we found that quantitative cultures do not fully represent the microbiome of DFUs when compared with genomic techniques.

We found that ulcer depth and duration are associated with microbial diversity and the abundance of specific pathogens. Deep
ulcers and those of longer duration have a more diverse microbiota, containing higher levels of anaerobes and Proteobacteria.
Superficial ulcers and those of shorter duration were associated with higher relative abundance of Staphylococcus. Pathare et al. (1998) found similar results in their study of DFU that used tissue cultures (35). Poor glycemic control was associated with ulcers containing high relative abundance of Staphylococcus and Streptococcus. No significant associations between overall bacterial diversity and individual abundant species were detected, suggesting
that unidentified dimensions of DFU bioburden, which are captured in EUC clustering, are responsive to glycemic control. Wound
tissue oxygen was not associated with DFU bioburden or community structure. However, enrollment excluded ischemic DFUs, which
undoubtedly have lower wound tissue oxygen levels and therefore may have a different bioburden and/or microbial community
structure. Similarly, necrotic tissue in the wound bed was not associated with bioburden. Most of the ulcers in the sample
were free of necrotic tissue, which decreased variability and the ability to detect differences. Neuropathic DFUs are frequently
free of necrotic tissue, so the sample in this study was typical of this type of chronic wound. Our findings of the association
of clinical factors with several measures of microbial community membership and structure suggest that metrics of this type
may be most important in determining the role of DFU microbiota on ulcer outcomes, such as healing or amputation, because
of the complex nature of the chronic wound microbiota. However, longitudinal studies that examine the dynamic relationship
between microbial community membership and structure with ulcer outcomes are needed to support this assertion.

Comparisons of traditional quantitative cultures with 16S rRNA gene sequencing raise some points of interest with regard to
the utility of using cultures in the clinical setting as a diagnostic tool to identify problematic bioburden. These comparisons
are needed because cultures remain more widely available around the globe than DNA sequencing technology, and their utility
needs to be identified in directing the management of DFUs. Historically, clinical practice for treating DFUs is based on
assumptions that implicate the dominant and/or culturable bacteria as the pathogenic/destructive bacteria. Similar to other
studies (7,12,36), we found that cultures do not fully represent bacterial diversity as compared with 16S rRNA gene sequencing. Microbial
load is widely viewed as the reference standard for determining problematic bioburden in chronic wounds that may not express
robust clinical signs (37). We demonstrated for the first time that cultures underrepresent the microbial load of the ulcer as compared with 16S rRNA
gene approaches. These findings may have important clinical implications justifying the clinical use of molecular approaches
rather than traditional cultures.

However, it is unclear if all organisms identified by 16S gene sequencing are important. Unlike cultures, dormant or dead
bacteria, which may or may not be contributing to an altered wound environment, are identified by 16S gene sequencing without
distinction between viable and nonviable organisms. Another limitation of this approach is that 16S gene sequencing only identifies
bacteria. It is quite possible that fungi, viruses, and/or other microeukaryotes may be important components of the DFU microbiome.
Third, profiling 16S rRNA genes only can tell us what is there and will not address the question, what is it doing? Future
studies will need to address the functional and mechanistic implications of colonization by specific microbes or microbial
communities.

No studies of purely neuropathic etiology were found to compare our findings. Although Dowd et al. (12) studied 40 DFUs using 16S rRNA sequencing, the location of ulcers reported in the study suggest a mixture of neuropathic,
ischemic, and mixed DFUs, and other ulcers that may be primarily arterial. This study found Corynebacterium to be the most prevalent bacterial taxa, followed by Bacteroides and Peptoniphilus. Price et al. (7) reported that Streptococcus was more prevalent in the wounds of persons with diabetes (n = 12) than those free of diabetes. Reasons for the discrepancy in findings between our study and these may be attributable
to differences in the ulcer samples.

Inconsistency in these findings highlights the need to describe and delineate the microbiome of wounds with homogenous etiology
and other pathophysiological mechanisms to determine differences in microbiome that may be driven by the wound environment
(36). In this way, the significance of the chronic wound microbiota can be compared and contrasted among chronic wounds of different
types. Furthermore, studies that take advantage of clinical metadata to appropriately stratify patient populations ultimately
will have the most potential to reveal causal links between microbiome variation and chronic wound outcomes using longitudinal
designs.

ACKNOWLEDGMENTS

This project was funded by National Institutes of Health (NIH), National Institute of Nursing Research (S.E.G., NINR R01 NR009448),
National Institute of Arthritis and Musculoskeletal and Skin Diseases (E.A.G., NIAMS R00 AR060873), and the NIH Intramural
Research Program (J.A.S). It was supported by the National Center for Research Resources and the National Center for Advancing
Translational Sciences, NIH, through grant UL1-RR-024979 (S.E.G.). The content is solely the responsibility of the authors
and does not necessarily represent the official views of the NIH.

No potential conflicts of interest relevant to this article were reported.

S.E.G., S.L.H., J.A.S., and E.A.G. designed the study. S.E.G., K.H., and E.A.G. performed experiments. S.E.G., K.H., and E.A.G.
analyzed the data. S.E.G. and E.A.G. performed statistical analyses and wrote the manuscript. S.L.H. analyzed the data and
performed statistical analyses. All authors contributed to editing the manuscript. S.E.G. and E.A.G. are the guarantors of
this work and take full responsibility for the integrity and accuracy of the data.

The authors thank the NIH Intramural Sequencing Center for their effort on this project. The authors thank David Margolis
(University of Pennsylvania), Heidi Kong (NIH), Sean Conlan (NIH), Sandra Daack-Hirsch (University of Iowa), Jeff Murray (University
of Iowa), Brian Schutte (University of Iowa), and members of the Grice laboratory (University of Pennsylvania) and Segre laboratory
(NIH) for their underlying contributions, useful discussions, and/or critical review of the manuscript.